This first indicator allows to measure the disparity in the learned representations of people by directly using the raw model embeddings.
If a model has higher P@1 for "male" than for "female", this indicator tells how much the model falsely recognizes a true female population as male does mis-gendering.
We show the results of this indicator in Table REF  and further measure the disparity between different genders and skintones in Table REF .
We make sevaral observations.
First, models pretrained on ImageNet-1K have a higher disparity.
Second, SEER models have the lowest disparity between different genders and skintones.
Finally, we observe that for SEER models, as the model size increases, the disparity decreases.
That means the model embeddings seem to recognize different genders and skintones more fairly.
We hypothesize that this is because SEER is pretrained on a very diverse dataset (see Fig. REF ) and the size of the model allows to better extract the salient information present in the image leading to better visual features.
The baseline models are trained on ImageNet-1K whose disparity has been empirically confirmed in previous work [1]}.
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